Large‐area yield prediction early in the growing season is important in agricultural decision‐making. This study derived maize (Zea mays L.) leaf area index (LAI) estimates from spectral data and used these estimates with a simple LAI‐based yield model to forecast yield under irrigated conditions in large areas in Sinaloa, Mexico. Leaf area index was derived from satellite data with the use of an equation developed with LAI measurements from farmers' fields during the 2001–2002 autumn–winter growing season. These measurements were correlated with the normalized difference vegetation index values from 2002 Landsat ETM+ (enhanced thematic mapper) data. The equation was then tested with 2003 Landsat imagery data. A yield model was validated with maximum LAI and yield data measured in farmers' fields in northern and central Sinaloa during three consecutive autumn–winter growing seasons (1999–2000, 2000–2001, and 2001–2002). The yield model was further validated with 2002–2003 autumn–winter ground LAI (gLAI) and satellite‐derived LAI (sLAI) data from 71 farmers' fields in northern and central Sinaloa. Grain yield was predicted with a mean error of −9.2% with maximum gLAI and −11.2% with sLAI. Results indicate that the yield model using LAI can forecast yield in large areas in Sinaloa in the middle of the growing season with a mean absolute error of −1.2 Mg ha−1. The use of sLAI in place of ground measurements increased the mean absolute error by 0.3 Mg ha−1. Nevertheless, the use of sLAI would eliminate laborious LAI measurements for large‐area yield prediction in Sinaloa.

tion on vegetation function as well as land-use cover (Tan and Shih, 1997; Fang et al., 1998;Jiang and Islam, The large-scale monitoring and estimation of crop yield is essential Ochi and Murai, 1999). The NDVI derived from for food security in Mexico. This study developed and validated a method of monitoring and estimating corn (Zea mays L.) yield by satellite-image data has been strongly linked to vegetameans of satellite and ground-based data. In autumn-winter 1999 and tion condition and plant biomass on the land surface spring-summer 2000, eight locations under irrigated and nonirrigated (Tan and Shih, 1997; Fang et al., 1998; Jiang and Islam, conditions in corn valleys of Mexico were localized by Global Position-1999; Ochi and Murai, 1999). Values for NDVI range ing Systems (GPS) and were sampled every 15 d. Photosynthetic from Ϫ1.0 to 1.0. Larger NDVI values indicate that the active radiation (PAR), leaf area index (LAI), crop development stage land surface is covered with dense healthy vegetation, (DVS), planting dates, and grain yield data were gathered from the while negative values indicate the presence of clouds, field. The normalized difference vegetation index (NDVI) was derived snow, water, or a bright nonvegetated surface (Yin and from NOAA-Advanced Very High Resolution Radiometer (AVHRR)Williams, 1997). A typical NDVI temporal profile for images. A growth model was developed to integrate satellite and healthy green vegetation rises as plant cover increases ground data. Net primary productivity (NPP) was estimated using PAR and NDVI. Dry weight increase (kg ha Ϫ1 d Ϫ1 ) was determined in spring, reaches a peak or plateau during summer, and considering NPP and the partitioning factor. Results indicated that declines with plant senescence in fall. the model accounts for 89% of the variability in yields under irrigatedCloud contamination that appears in virtually every conditions and 76% under nonirrigated conditions. The methodology AVHRR scene decreases NDVI values; therefore, daily seems advantageous in large-scale monitoring and assessment of corn NDVI images in a continuous time series do not always yield.

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Assessments of impacts of future climate change on widely grown sugarcane varieties can guide decision‐making and help ensure the economic stability of numerous rural households. This study assessed the potential impact of future climatic change on sugarcane grown under dryland conditions in Mexico and identified key climate factors influencing yield. The Agricultural Land Management Alternatives with Numerical Assessment Criteria (ALMANAC) model was used to simulate sugarcane growth and yield under current and future climate conditions. Management, soil and climate data from farm sites in Jalisco (Pacific Mexico) and San Luis Potosi (Northeastern Mexico) were used to simulate baseline yields. Baseline climate was developed with 30‐year historical data from weather stations close to the sites. Future climate for three decadal periods (2021–2050) was constructed by adding forecasted climate values from downscaled outputs of global circulation models to baseline values. Climate change impacts were assessed by comparing baseline yields with those in future decades under the A2 scenario. Results indicate positive impacts of future climate change on sugarcane yields in the two regions, with increases of 1%–13% (0.6–8.0 Mg/ha). As seen in the multiple correlation analysis, evapotranspiration explains 77% of the future sugarcane yield in the Pacific Region, while evapotranspiration and number of water and temperature stress days account for 97% of the future yield in the Northeastern Region. The midsummer drought (canicula) in the Pacific Region is expected to be more intense and will reduce above‐ground biomass by 5%–13% (0.5–1.7 Mg/ha) in July–August. Harvest may be advanced by 1–2 months in the two regions to achieve increases in yield and avoid early flowering that could cause sucrose loss of 0.49 Mg ha−1 month−1. Integrating the simulation of pest and diseases under climate change in crop modelling may help fine‐tune yield forecasting.

Crop models with well-tested parameters may help improve sugarcane productivity for food and biofuel generation, especially in rainfed areas where studies are scarce. This study aimed to calibrate crop parameters for the sugarcane cultivar CP 72-2086, an early-maturing cultivar widely grown in Mexico and other countries, and evaluate their adequacy in simulating sugarcane in a diverse range of rainfed conditions. For the calibration and evaluation of parameters, the ALMANAC model was used with climate, soil, management, and yield for two growing seasons from 30 farms in three regions (Northeastern Mexico, Gulf of Mexico, and Pacific Mexico). Statistical analyses were made using regression analysis and mean squared deviation and its three components, i.e., the squared bias, the lack of correlation weighted by the standard deviations, and the squared difference between standard deviations. Model simulations with a light extinction coefficient (k) of 0.69, maximum leaf area index of 7.5, leaf area index decline rate of 0.3, optimal and minimum temperature for plant growth of 32 °C and 11 °C, respectively, potential heat units of 6000 to 7400 degree days (base 11 °C), harvest index of 0.9; maximum crop height of 4.0 m, and root depth of 2.0 m showed highest accuracy and captured best the magnitude of yield fluctuations with a root mean squared deviation of 7.8 Mg ha−1. The parameters were found to be reasonable to use in simulating sugarcane in diverse regions under rainfed conditions. Using a dynamic value of k (varying during the growing season) deserves further study as it may help improve crop model precision.

Intensive cropping systems based on mechanical movement of soil have induced land degradation in most agricultural areas due to soil erosion and soil fertility losses. Thus, farmers have been increasing fertilization rates to maintain an economically competitive crop yield. This practice has resulted in water quality degradation and lake eutrophication in many agricultural watersheds. Research was conducted in the Patzcuaro watershed in central Mexico to develop appropriate technology that prevents nonpoint source pollution from fertilizers. Organic matter (OM) and nitrogen (N) losses in runoff and nitrate (NO3‐N) percolation in Andisols with corn under conventional till (CT) and no‐till (NT) treatments using variable percentages of crop residue as soil cover were investigated for steep‐slope agriculture. USLE type runoff plots were used to collect water runoff, while suction tubes with porous caps at 30, 60, and 90 cm depth were used to sample soil water solutes for NO3‐N analyses. Results indicated a significant reduction of N and OM losses in runoff as residue cover increased in the NT treatments. Inorganic N in runoff was 25 kg/ha for NT without residue cover (NT‐0) and 6 kg/ha for the NT with 100 percent residue cover (NT‐100). Organic matter losses in runoff were 157 and 24 kg/ha for the NT‐0 and NT‐100 treatments, respectively. Nitrate‐N percolation was evident in CT and NT with 100 percent residue cover (NT‐100). However, NT‐100 had higher NO3‐N concentration at the root zone, suggesting the possibility of reducing fertilization rates with the use of NT treatments.

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